Computational Intelligence for Congestion Control and Quality of Service Improvement in Wireless Sensor Networks

Abstract

Congestion and quality of service are widely researched topics in Wireless Sensor Networks in recent years. Many researchers proposed and compared the merits and demerits of various algorithms with the existing algorithms. The major challenge lies in developing an algorithm which optimizes the various performance parameters like packet drop ratio, residual energy and throughput of the network. Focus of the present work is to reduce congestion and improve quality of service by applying various metaheuristic or computational intelligence techniques which can optimize performance parameters. An objective function is formulated on the basis of factors like residual energy, throughput, distance between nodes and the number of retransmissions and its value is optimized by using various nature inspired computational intelligence techniques and their results are compared. Simulation results have shown that water wave algorithm outperforms all the other algorithms on the basis of packet drop ratio and throughput of wireless sensor network.